PC-ORD Reviews

PC-ORD Version 7 Testimonials

by Dr Winfried Voigt
University of Jena, Germany

Thank you for the wonderful news that v. 7 has now been released. Even though
practically every multivariate statistical technique/method could alternatively be done
using R language, I still prefer using PC-ORD for teaching ecology students multivariate
statistics. PC-ORD is very user-friendly and offers, in just one self-contained
package, many powerful methods and a great toolbox (Modify Data) that ecology
students really need and can quickly utilize in their research. An excellent feature
is the textbook-like, built-in documentation explaining theoretical background in detail.

We also offer courses on how to use R efficiently, but there are always students without
experience or who have not attended such a course, or regardless, still have problems with
R and so in a classroom setting, they hold back progress. Hence, in practical,
hands-on courses that are also quite limited in time, students using PC-ORD can focus much
more on the statistical and ecological background rather than by spending too much time
with programming. The objective of my courses is the understanding of the process of
multivariate data analysis and not so much on the technical part, even the latter results
in a more thorough understanding of the procedures applied. When students understand
why and how to effectively do multivariate statistical analyses of their data, they can
then use, with little effort, other programs or packages as well.

by Dr. Peter R. Nelson
University of Maine at Fort Kent, USA

In the world of increasing use of open-sourced statistical platforms like R, some might
wonder if there is a need for proprietary stats programs. Despite this trend, I find
myself still going back to PC-ORD, even though I also use R, Python and other scripting
platforms. The reason I keep using PC-ORD is simple: PC-ORD is clearly superior to
trying to code the same functionality in R or other popular open-source analytical
platforms. Another reason I keep using PC-ORD is the great
documentation that is almost like a course in multivariate stats built in to the
software. You can find answers to many of the common questions or even detailed
descriptions regarding most of the algorithms used in the software, all of which has
citations to primary literature for the even more curious user. The latest version
builds on an already large repertoire of great multivariate functionality.

For me, the most notable addition in functionality in PC-ORD 7 are modules that enable
trait-based analysis with totally different approaches, including the fourth-corner
analysis and Hilltop plots. Both of these techniques are relatively recent but so
far infrequently used methods to analyze how traits vary in a community with respect to
environment. Trait-based analyses are exploding in frequency of use across
ecological and evolutionary biology journals so these additions to PC-ORD will definitely
be very useful to anyone using traits in their research. PC-ORD 7 incorporates these trait
analysis tools into a series of relatively easy to use set of prompts so that the user can
both do the analysis but, equally important, understand what the trade-offs are when using
different settings. For example, the associated trait-multiplication tool clearly
describes how weighting the multiplication of the trait matrix by the species matrix may
influence the outcome of downstream analysis and the resulting ecological interpretation.

One interesting new feature is the addition of NMS stress plots while the algorithm runs.
This real-time display of each run of the algorithm, both with real and randomized
data, to give the user a qualitative but useful assessment of their ordination. PC-ORD 7
also implements a new file format (.mjm) but all older file formats (.wk1) are easily
converted. File importing from various file formats, such as .csv, .xls and .xlsx,
isnt new to PC-ORD 7 but the consistency and ease of import seems to have been
improved as Ive found myself having fewer unknown import errors.

by Dr. Emily A Holt
University of Northern Colorado, USA

The newest version of PC-ORD (v. 7) has several additional features since the last
version was released just over five years ago (e.g., a separate trait matrix and
associated toolset, batch commands, a novel file format, and new data modification
options). However, what remains intact is the user-friendly functionality of a
powerful multivariate software package. MjM has done a stunning job of responding to
user feedback and integrating current techniques with each new version. Even small
changes, such as color coding categorical and quantitative column titles, makes the
program more intuitive and less prone for user-induced problems. PC-ORD continues to
be the go-to multivariate software for seasoned ecologists in need of advanced techniques,
novices with no statistical background, and everyone in between. The extensive help
menu, direct links to the user-driven online Discussion Group, and the Advisor Wizard
allows even undergraduate students to manage and analyze community datasets appropriately
and with confidence. Several new features (Fourth Corner Analysis, Fuzzy Set Ordination,
Contour and Hilltop Overlays) extend our toolbox to further investigate functional traits
and non-parametric relationships between secondary variables and ordination scores, using
the HyperNiche framework. With this seventh version, PC-ORD offers new tools to
analyze community data while maintaining itself the reliable and accessible multivariate
software of choice. As a dedicated user for nearly 15 years, each new version of
PC-ORD is the unwavering choice to analyze the data in my lab, and encourages my students
and I to think about multivariate data in new ways.

"PC-ORD is recommended strongly for many undergraduate and
postgraduate students and particularly for those attempting to use multivariate analysis
of vegetation data for the first time. This spreadsheet-based software (Excel(R) or
Lotus 1-2-3(R)), now in its 6th version (2010), includes methods for virtually all the
methods of multivariate analysis covered in this text... Two particularly important
features are the ability to produce publication-quality graphics and the availability of a
3-level autopilot mode for non-metric multidimensional scaling (NMS). The latter is a
particularly strong asset. The program can handle very large data sets."

"Given its range of applications and excellent supporting materials it is very
reasonably priced and there are generous discounts for student users. Purchasers can also
obtain the HyperNiche multiplicative habitat modelling package for non-parametric
regression... at a significant discount."

"Most important of all, however, is the availability of a step-by-step introductory
guide for students (Peck, 2010) and a comprehensive manual (McCune and Grace, 2002),
linked to an online help system within the software itself."

The recent version 6 release of the software PC-ORD Multivariate Analysis of Ecological
Data (MjM Software, www.pcord.com) earns a solid A grade for ease of use,
comprehensiveness of analysis tools, and excellent graphing capabilities. New features in
version 6 include the addition of several analysis tools (PCoA, RDA, SumF), the
enhancement of others (partial Mantel test, orthogonal rotation in NMS), additional
graphing features (boxplots, convex hulls) and new options that finally make importing
data from Excel easy.

Description

The combined effort of Dr. Bruce McCune (Oregon State University) and MjM Software, the
PC-ORD software is an integrated compilation of data management, exploration, graphing,
and analysis tools applicable to the kinds of data collected by ecologists. Data are
stored in a spreadsheet format that can accommodate up to 32000 rows and columns (23170 if
a distance matrix will be calculated) and can be imported directly from an Excel
spreadsheet or from text files of various formats. Options are available to assist users
with structuring their data matrices (e.g., appending matrices, deleting rows and
columns), summarizing data (e.g., sums, diversity indices, species lists), graphically
exploring data (e.g., boxplots, scatterplots, distribution curves), and modifying data
(e.g., common transformations and relativizations). Analysis tools, with both reasonable
default settings and the ability to tailor input parameters as needed, are included to
address both common and complex analyses questions:

How does species composition vary along known gradients?
o polar ordination (Bray-Curtis
ordination)

How can sample units be ordered based on the relative abundance of indicator species?
o weighted averaging ordination

How does species composition or habitat condition vary among samples taken along unknown
gradients?
o principal components analysis and
nonmetric multidimensional scaling, as well as reciprocal averaging, detrended
correspondence analysis, and principal coordinates analysis

How can new sample units be ordered to fit within the space of an existing ordination
model?
o predictive nonmetric multidimensional
scaling (NMS scores)

Does species composition or habitat condition vary among locations or change following a
disturbance or as a result of applying a treatment?
o multiresponse permutation procedure,
distance-based MANOVA, SumF

Which species behave similarly across a dataset?
o agglomerative clustering, two-way
clustering, TWINSPAN

Which samples units can be classified into the same groups based on species or habitat
data?
o agglomerative clustering, two-way
clustering

Which species are most abundant and frequent in treatment or habitat groups?
o indicator species analysis

Do two datasets show similarity of compositional structure?
o Mantel test

Review

Although the menu-driven user interface is easy enough to navigate and the help menu is
extensive, the first hurdle is simply getting the data into PC-ORD. Because the software
operates on *.wk1 formatted spreadsheet files, which Microsoft Excel no longer supports,
most users will not be able to simply open their data files but must instead
import them from an Excel file or from a text file that has been exported from
some other software (e.g., a database). Fortunately, version 6 now includes options to do
this relatively painlessly. However, once the data are in PC-ORD, which only saves files
in the *.wk1 format, users must export the data if they wish to make complex edits using
Excel (such as using formulas). Most users will require a few back-and-forths before they
get their data just they way they want them. From there out, use of the drop-down menus is
straightforward and strategically placed help buttons can answer many
questions. Users who are not sure where to start may take advantage of the advisor
wizard, which asks a series of questions to lead users through a dichotomous key to
determine which data manipulations or analyses are most appropriate. Although the
questions posed by the wizard may be difficult for beginning users, they force the user to
think through important decisions in their analysis pathway, and help is provided in the
form of page numbers for relevant reading in the 2002 Analysis of
Ecological Communities book by Bruce McCune and James Grace.

Once users have their data in the software and have become accustomed to the interface,
they will be pleasantly surprised as the range of tools available in PC-ORD that tailor to
ecological datasets. While basic data management options are annoyingly lacking (e.g.,
copy and paste, aggregating across subsets of data), PC-ORD includes tools useful for
ecologists that are not commonly found in statistical software packages, such as the
ability to construct species area curves, plot smoothed variable distributions, create
species lists, or conduct analyses such as polar ordination (which they term
Bray-Curtis ordination), indicator species analysis, and SumF. In addition,
all of the most common ordination, classification, and group-testing tools are available
and many can be run using default settings or easily tailored to fit dataset constraints
and analysis objectives. For example, simple mouse clicks in the setup window are all that
is needed to run a PCA with a variance/covariance cross-products matrix, include biplot
scores for species, and add a randomization test. Similarly, NMS can be run using default
settings (in Autopilot mode) or the user can define input (e.g., number of
axes) and output (e.g., orthogonal rotation) settings. The information provided following
most procedures in the result file is so extensive that the beginning user may
be a bit overwhelmed, but more advanced users will appreciate having access to details
that can be cumbersome to program in other software.

Almost all users will find the graphics interface a welcome contrast to those available in
the most common statistical software packages. After running procedures that produce
output that can be graphed (e.g., ordinations, clustering dendrograms, species area
curves), users enter a graphics interface that is like a program within a program, in that
it is not possible to access the data files or main program menus while graphing output.
Once there, however, drop-down menus or toolbars provide many options for viewing output
optimally and extensive user preference options can be used to tailor graphics precisely
for the desired medium. Nice features include the ability to define and save preference
sets that control features such as plot type, fonts, colors and line and symbol types:
users can apply one set (e.g., in black and white) for manuscripts and another (e.g., in
color) for presentations.

Upgrading from version 5 to version 6 is probably worth it. A notable improvement is the
ability to now import/export spreadsheet data (as *.xls Excel files) without including the
special formatting information that PC-ORD requires at the top of the datafile. In
addition, several new analysis tools have been added, including the metric
multidimensional scaling procedure principal coordinates analysis (PCoA), the linear
equivalent to CCA in redundancy analysis (RDA), and a permutation test based on aggregated
F-statistics called SumF. Version 6 also includes additional graphing options, such as
creating boxplots and drawing convex hulls, which connect the dots around
groups in ordination space (Figure 1).

Many of the tools within PC-ORD are available in other software. For instance, other
commercial products have comparable versions of PCA (CANOCO, PRIMER, SAS, S+, etc.) and
clustering (PRIMER, SAS, SPSS, S+, etc.), and these and other tools are also available in
freeware (e.g., the VEGAN packing in R). While a few users may prefer options only
available in other software packages (e.g., including covariates in CCA within CANOCO,
applying distanced-based MANOVA to some unbalanced designs in PRIMER, or exploring species
area relationships in depth in EstimateS) the vast majority of users will find the tools
available in PC-ORD to be equivalent to, or better than, versions available elsewhere. For
instance, PC-ORD provides users the greatest flexibility available for running NMS and
provides the most detailed output, necessary for the proper interpretation of this complex
tool. The single greatest argument for PC-ORD, however, is simply that most of the
multivariate analysis tools needed to explore species datasets are present in the same
software package, eliminating the time consuming need to learn multiple programs, convert
datasets to different formats for software entry, and search around for the best tool for
making publication quality graphics.

As such, PC-ORD is competitively priced for student and single user licenses; site
licenses, however, can be quite expensive as the fee increases linearly with the number of
potential simultaneous users. Although available to order online, the software is not
downloadable, which netbook users (and procrastinators) may find inconvenient. Mac-only
users are also out of luck, as PC-ORD only runs on the Microsoft Windows platform (Windows
98 and later). The software does not come with a manual, but the extensive help menu
contains a wealth of descriptions, explanations, equations, and citations. In addition, a
new companion book is now available (Peck 2010) that
guides beginning users through the analysis process using the tools available in version
6.

Recently, version 5 of PC-ORD, one of the major commercial software packages for
multivariate ecological community data analyses, was released. The new version offers a
whole range of techniques and methods for analyses of ecological data. It includes modules
for different types of ordination and classification, as well as other exploratory
techniques such as species-area curve analysis and indicator species analysis. Data are
stored in spreadsheets and can be easily manipulated in various ways. In essence, version
5 of PC-ORD offers the user a full toolbox for exploration and analysis of ecological
data, packed in a user-friendly environment.

Description

Recently, a new version of PC-ORD, a software package for multivariate analysis of
ecological data has been released. This package, developed by Bruce McCune and others
(McCune & Grace 2002) is one of the major commercial software packages for
multivariate ecological community data analyses. The new version 5 includes both
enhancements of existing analyses as well as new features. Among the new features is an
extended graph module with possibilities for 3D ordination plots, two-way cluster
dendrograms, dominance-diversity curves and frequency-abundance plots, and frequency
distributions. The main improvements to the previous graph module are better options for
editing graphs, and increased export options. The previous tray of analyses is extended
with permutation-based MANOVA with one-way, factorial, nested, and blocked designs,
two-way cluster analysis, smoothed univariate frequency distributions, and a function that
displays the most important summary features of a data set. The previous analyses are
enhanced with randomization tests for PCA, cluster analysis directly from a distance
matrix, writing of a distance matrix to spreadsheet or text file, and an option to break
down row and column summaries by a variable in the second matrix. To help users to select
the appropriate analysis, an advisor wizard, based on a decision tree, is added. Data
management and import/export has been improved. Version 5 allows for example simultaneous
adjustment of main and second matrices, and filtering rows by a criterion variable.

Review

Once the new user has become acquainted with the somewhat antiquated way of entering data,
PC-ORD version 5 offers a wide variety of tools for exploring data and testing hypotheses
in community ecology. The software is a collection of classical as well as more novel
statistics, used in numerical ecology. In addition to a variety of ordination and
classification techniques, the program also includes modules for testing group identity,
constructing species-area curves, Mantel tests and non-parametric MANOVA.

The interface is intuitive and easy to understand. It is easy to keep track of different
datasets and variables through complex analyses in several steps. There are a number of
possibilities for data transformation, manipulation and permutation. In all analyses,
results from intermediate calculations as well as final results are written to a results
window that can be saved. Additionally, ordination scores are written to a separate file,
which facilitates export.

For ecologists, multivariate statistical methods may be divided into hypothesis generating
(i.e. exploratory), and hypothesis testing methods (Økland 1996). Version 5 of PCORD
offers a wide variety of both types. The exploratory, or indirect, type of methods
includes traditional analyses such as principal components analysis, correspondence
analysis, and detrended correspondence analysis. In addition, there is an array of methods
for summarising and inspecting data, including e.g. calculation of diversity indices and
outlier analysis. Interesting and useful additional exploratory techniques include
species-area curves analysis and indicator species analysis (Dufrêne & Legendre
1997).

The hypothesis testing, or constrained, methods include both multidimensional scaling as
well as X2-based methods such as canonical correspondence analysis.
There are options for permutation tests of group identity but there is no option for
testing the significance of individual explanatory variables prior to a constrained
ordination. However, the graph module offers an elegant way of inspecting the contribution
of the individual explanatory variables. In ordination, PC-ORD can plot the relationship
between an ordination axis and individual species as well as explanatory variables.

For classification, PC-ORD offers a wide variety of tools. In the modules for both one-
and two-way hierarchical classification, a user may choose among many combinations of
distance measures and agglomeration techniques. The classical method TWINSPAN (Hill 1979)
is also included.

A new feature in the current version is a dichotomous decision tree for helping users to
select an appropriate method. The intentions behind this tree are obvious, but to be able
to answer the sometimes quite complex questions, the user has to be very familiar with
multivariate methods. My feeling is that a user who has the experience to be able to
answer the questions probably does not need the decision tree. Anyhow, for a user that has
just started using these techniques, the tree may be of great help, given that the user
knows the nomenclature. A more advanced user may use the tree to explore the capabilities
of the program.

Another interesting feature is the possibility of including your own programs as add-in
tools. In the standard installation, a program for calculating degree of nested-ness
(sensu Patterson & Atmar 1986) is included. This option may not be the most important
feature for a new or intermediate user, but is a means for the more advanced user to
personalise the program.

The graph module is easy to use and offers a user to view ordination results in both two
and three dimensions. An interesting feature is the possibility of drawing successional
vectors in ordination diagrams. Results of classifications are illustrated with
dendrograms in one or two dimensions, with scales showing distance, and remaining
information along a hierarchical tree. Produced graphs are of publication quality and can
be saved in a number of formats. There are numerous options for personalizing a graph,
including varying symbol sizes, labels, vectors, grids, and construction of joint plots.

Documentation of the program is only provided as comprehensive help files obtained from
within the program. The content of the help files is sufficient, with both examples as
well as theoretical background for the different techniques included in the program.
However, many users would probably prefer the documentation as a printed hardcopy.

PC-ORD can only be run under the operating system Windows, version Win98 or higher. The
program can accept data matrices with more than 500 million elements, or a maximum of
32000 columns or rows. This is probably larger than most ecological datasets. The price
for a single user licence is competitive compared to other similar commercial software. A
site licence is on the other hand relatively expensive as the cost increases with the
number of users. The website (www.pcord.com) offers online ordering, but the program
cannot be downloaded.

Many of the techniques and modules included in PCORD can also be found on the Internet as
self-standing freeware. VEGAN (Oksanen 2006) and Ginkgo (Font et al. 2006; see Bouxin
2005) are examples of free software for multivariate techniques, written for ecologists.
The PC-ORD module for species-area relationships is a light version of the freeware
EstimateS (Colwell 1997). TWINSPAN and IndVal which both are included in PCORD are also
available for free. However, in PC-ORD most necessary techniques for exploring and
analysing ecological data are collected in one common frame, with no need for repeated and
time-consuming data preparation for several programs.

In summary, PC-ORD offers a wide range of tools for analysing ecological data in a
user-friendly environment.

Colwell, R.K. 1997. EstimateS: Statistical estimation of species richness and shared
species from samples. Version 5. User's guide and application. Published at:
http://viceroy.eeb.uconn.edu/estimates.

I have published four other papers that used PCORD-generated graphics, as well as
another that used TWINSPAN on PCORD to identify ecologically related groups. I have
also used the Bray/Curtis program on PCORD to generate similarity indices for several
papers. Needless to say, I cant say enough about how useful PCORD has been.
The new version should help even more.

Ethan Bright, Ph.D. Candidate
School of Natural Resources and Environment
The University of Michigan
Ann Arbor, Michigan, USA

I predict PC-ORD 5 will be a well-received improvement on the previous version.
Besides improving the program's statistical and graphical routines, the addition of an
"analytical wizard" and its ability to keep track (with a text file) of the
decision-making process make this an invaluable resource for both student and professional
alike.

PC-ORD is a software package for multivariate analysis and
classification of ecological data. The DOS version (version 2) was reviewed in the January
1996 ESA Bulletin, and the first Windows (16-bit) version (version 3) was
reviewed in the April 1998 ESA Bulletin. In early 1999, MjM released the 32-bit
product (version 4), reviewed here, which is no longer compatible with Windows 3.x, and
like most new releases, demands more memory and disk space than earlier versions. If
you're no longer using Windows 3.x, upgrading to PC-ORD version 4 has significant
advantages over version 3.

Version 4 of PC-ORD requires an 80486 or better CPU, which means
it could run on the new computers in the Hubble Telescope, but it's unlikely you could run
Windows 95/98/NT efficiently on an 80486 CPU. The software occupies about 5.5 Mb of hard
disk space and uses a minimum of 8 Mb RAM. PC-ORD will use all available memory for matrix
operations, so the previous 16 Mb limit on matrix size has been removed. The only
remaining constraint to matrix size is that the default format for matrices, *.wk1 (Lotus
version 2.0), allows matrices no larger than 32,000 rows x 32,000 columns.

Available analysis routines fall into two broad groups: ordination and
classification. Of the routines in Table 1, blocked multiresponse
permutation procedure (MRBP) and weighted averaging are new to version4. Nonmetric
multidimensional scaling (NMS) has been significantly enhanced to include an
"autopilot mode" that speeds through multiple runs and significance tests, and a
"predictive-mode" NMS that calculates scores for new data points based on prior
ordinations.

Plotting of species in ordination space, by using weighted averaging to
calculate their scores, is now available in NMS, Principal Components Analysis (PCA), and
Bray-Curtis ordinations. Distance measures available include Euclidean (raw, squared, and
relativized), Sorenson (raw and relativized), Jaccard, correlation, and chi-squared. In
addition, data summaries (mean, SD, sum, minimum, maximum, skewness, kurtosis, CV, species
richness (S), Shannon-Weiner diversity (H'), Shannon-Weiner evenness (H'/ln[S]),
and Simpson's index of diversity (D) can be calculated for rows (sites) or
columns (species). Identification of outliers (matrix rows or columns) based on all
distance measures is accomplished by a separate routine. Basic species-area analysis for
determining adequacy of sampling is also included.

Beginning with version 3, PC-ORD produced publication-quality graphs
from most routines. These have been rounded out in version 4, which includes
publication-quality graphs for cluster analysis (dendrograms), species-area curves (with
confidence bands), and NMS scree plots. Graphics files are output as *.emf
(windows-enhanced metafiles) or *.bmp (bitmapped). Data management has also improved in
version 4: spreadsheets can be edited (albeit without full Windows capabilities), data
transformed or relativized, matrices transposed or multiplied, rows or columns deleted
(based on user-defined criteria, such as emptiness or sparseness), shuffled (randomized),
or smoothed. Acceptable formats for input data files remain small (*.wk1 spreadsheet,
PC-ORD compact format, PC-ORD version 1 format, DECORANA/TWINSPAN condensed format, list
format, and comma-separated values (CSV) format), but are easily created with ASCII text
editors or spreadsheet programs. Finally, like many new statistical packages, PC-ORD saves
work as a "project" (*.prj) file, which is really a set of associated
files (options, settings, matrices, results, graphics) produced by PC-ORD. This
facilitates organization of a set of analyses and increases efficiency, because options
and settings do not have to be re-entered at the start of each session. Individual files
can still be saved one at a time.

PC-ORD is still one of the most easily used, comprehensive packages for
multivariate analysis of ecological data. Many of the routines are unavailable in standard
statistical packages (which at best usually provide only PCA and cluster analysis). The
version 4 user's manual provides somewhat more information on the pitfalls of different
techniques and options than earlier manuals, but still assumes general familiarity with
the literature on multivariate methods. Routines in PC-ORD are current, and the authors
are quick to correct bugs and revise algorithms as new ideas are published. Incremental updates and patches are available free from their
web site <http://:www.pcord.com>. The package is reasonable
priced and should be considered strongly for research and teaching applications.

Hill, M. O. 1979b. TWINSPAN--A FORTRAN program for arranging
multivariate data in an ordered two-way table by classification of the individuals and
attributes. Section of Ecology and Systematics, Cornell University, Ithaca, New York, USA.

Table 1. Analytical methods available in PC-ORD
version 4 for multivariate ordination and classification

Type and method

Algorithm

Ordination

Bray-Curtis

Bray and Curtis (1957), Beals (1984)

Canonical Correspondence Analysis (CCA)

ter Braak (1986) with corrections of Okasanen and Minchin
(1997)

Detrended Correspondence Analysis (DCA)

Hill (1979a) with corrections of Okasanen and
Minchin (1997)

Nonmetric Multidimensional Scaling (NMS)

Mather (1976)

Principal Components Analysis (PCA)

Grieg-Smith (1983)

Reciprocal Averaging

Hill (1979a)

Weighted Averaging Classification

Whittaker (1967)

Classification

Cluster Analysis

Multiresponse Permutation Procedures (MRPP)

Mielke (1984)

Blocked MRPP (MRPP)

Mielke (1984)

Two-way Indicator Species Analysis (TWINSPAN)

Hill (1979b)

Indicator Species Analysis

Durêne and Legendre (1997)

Mantel test

Mantel (1967)

PC-ORD version 4 Testimonials

Chris Ulrey
Plant Ecologist
National Park Service
Blue Ridge Parkway

I've been using this program quite a lot with a dataset consisting of 2300 plots and
1000 species. The NMDS procedure sometimes produces spherical-like solutions, but can
usually be resolved by increasing the number of iterations, dimensions, or number of runs.
The NMDS procedure has been improved greatly from previous versions and should accommodate
the power user who likes total control to the generalist who just wants the bottom line.
The cluster analysis routine is very nice. Especially, the option for outputting a range
of grouping variables into the secondary matrix. The dendrogram graphic is excellent, and
particularly useful when symbolized by variables in the second matrix. The ability to edit
either matrix is GREAT! No longer does one have to go between a spreadsheet and PCORD. The
2 new import options have added to the PCORD's flexibility. In my case, the 'database
list' option was the only way I could get a large dataset into PCORD (short of creating a
ccf file in SAS).

Mark Higgins
Institute of Ecology
University of Georgia

Every analysis I have needed to perform (from Mantel tests, to summaries, species-area
curves, ordinations of all varieties) was available with PC-ORD. And to top it off, the
interface is absolutely excellent. Working with this application has been a dream.

Jeffrey Ostermiller
Graduate Student USU

A few months ago, my Advisor, Chuck Hawkins, purchased a copy of PC-ORD and I am
impressed! I have found the program to be more flexible with more readily interpretable
data than any other software that I have used for community-level analyses (i.e. CANOCO,
PATN, SPlus). As a result, I am interested in purchasing a copy for my personal use.

Dean Urban
Duke University

Well, the dust has settled on my first semester of teaching with PC-ORD. I am really
happy with this version: you should be quite proud of this beast! I've tried teaching this
stuff on IBM mainframes (with code ported from the Cornell ecology program!), in SAS,
SPSS, S-plus, and I've been using PC-ORD since your first version (I think). This latest
is a joy. Even my students who have no reason to know otherwise seem to realize that
they're being spoiled.

Mario E. Biondini
Professor
North Dakota State University

I have used PC-ORD for homework exercises in my graduate level course class (for Ph.D
students) as a result of my adoption of McCune, B. and J.B. Grace (2002; Analysis of
Ecological Communities; MjM Software Design, Oregon). The didactic results of using McCune
and Grace (2002) have been excellent, so I definitely plan to adopt it for both my
graduate and undergraduate. I recommend to all of my students to buy their own versions
PC-Ord since they will need this software in their research and is much more affordable
and versatile than CANOCO.

PC-ORD Version 3 Review

Bulletin of the Ecological Society of America 79:144. (1998)

by Dr. David Inouye

The DOS version of PC-ORD (version 2.0) was reviewed in the January 1996
ESA Bulletin. In the summer of 1997, MjM released a Windows version (16-bit, not
Windows-95 native) with significantly improved capabilities relative to the DOS version.
Like the earlier version, PC-ORD will run on CPUs as primitive as the 80286, although it
is unlikely that Windows will actually run on that chip. PC-ORD occupies just under 4 Mb
of hard disk space, and requires a minimum of 4 Mb of RAM. Unlike the earlier version,
analysis is now limited by available RAM, although matrices larger than 16,000 x 16,000
are not permitted. The built-in "memory requirements" option allows you to
calculate needed RAM for a given matrix size; it estimates nearly 2 Gb of RAM would be
necessary for operation on a 16,000 x 16,000 matrix!

Version 3.0 retains the multivariate routines available in version 2.0 (Bray-Curtis
ordination, canonical correspondence analysis, detrended correspondence analysis,
nonmetric multidimensional scaling, principal components analysis, reciprocal averaging
ordination, cluster analysis, multiresponse permutation procedures, and two-way indicator
species analysis), along with simple analysis of species-area curves. It also adds a
routine for multiway indicator species analysis (Dufrene and Legendre 1997), a Mantel test
to test for no relationship between dissimilarity or similarity matrices (Sokal and Rohlf
1995; see McCune and Allen 1985, Burgman 1987, and Tuomisto et al. 1995 for ecological
examples), and more robust algorithms for detrended correspondence analysis, canonical
correspondence analysis, and TWINSPAN ordination (see Okansen and Minchin 1997). The
documentation for canonical correspondence analysis has been significantly updated, and
also describes the Monte Carlo (randomization) test now available in PC-ORD to test for
significant relationships between matrices, and significant structure in the main matrix.
Similar Monte Carlo routines are implemented for indicator species analysis.

Other new additions to version 3.0 include the ability to create and work with species
lists, and most importantly, to create publication-quality graphics. Many customization
options are available for graphical output, including: frame type, symbols, coding by
groups, line thickness, point labeling, graph and axis titles, tick marks and labels,
fonts, and color.

PC-ORD remains one of the most user-friendly, comprehensive packages for multivariate
analyses used commonly by ecologists. The user manual assumes general familiarity with
methods for multivariate analysis, and both the clearly written, well-referenced manual
and excellent online help focus principally on software use. The software is up to date
with current developments in ecological multivariate analysis: it would be nice to see
additional work on the species-area analysis routines updated to current ideas as well
(cf. Leitner and Rosenzweig 1997). Despite the idiosyncratic format required for input
matrices (presumably a holdover from the original Cornell Ecology Programs), any community
ecologist should seriously consider adding PC-ORD to their software toolbox.

Literature cited

Burgman, M. A. 1987. An analysis of the distribution of plants on granite outcrops in
southern Western Australia using Mantel test. Vegetatio 71:79-86